Snowflake SPS-C01 : Snowflake Certified SnowPro Specialty - Snowpark

A Snowpark application needs to process a large dataset (1 TB) residing in Snowflake, performing complex transformations. Due to network constraints and the complexity of the transformations, the execution takes a considerable amount of time. The application's end-users are complaining about the latency. Which of the following strategies would MOST effectively enhance performance, specifically targeting the reduction of overall execution time considering the synchronous vs. asynchronous execution models and the implications of the 'block' parameter?

• A. Employ asynchronous actions with 'block=True' to immediately retrieve results, ensuring data consistency but potentially increasing overall latency due to blocking.
• B. Utilize synchronous actions to trigger transformations and retrieve results; this approach guarantees immediate availability but might not be optimal for long-running, complex operations.
• C. Increase the Snowflake warehouse size to improve compute capacity, regardless of the synchronous or asynchronous execution model.
• D. Minimize data transfer by applying aggressive filtering and aggregation within Snowflake before transferring data to the Snowpark application for further processing, and use synchronous action with a small warehouse size.
• E. Implement asynchronous actions with 'block=False' to initiate transformations without waiting for completion, allowing other parts of the application to proceed concurrently. Subsequently, use a mechanism (e.g., polling, callbacks) to retrieve the results when available, leveraging parallel execution to enhance overall responsiveness.

Consider the following Snowpark Python code snippet designed to aggregate sales data by region:

During testing, you observe that the performance of this code is suboptimal, especially when dealing with very large 'SALES DATA tables. Using Snowflake's query history, you notice that a significant amount of time is spent on data shuffling during the operation. What optimization strategies could you employ within this Snowpark code to minimize data shuffling and improve the overall performance?

• A. Repartition the 'sales_df DataFrame using 'repartitionBy' before the 'groupBy' operation, specifying the 'REGION' column to ensure that data for each region is co-located on the same node.
• B. store 'SALES_DATX as a clustered table using the 'REGION' column.
• C. Utilize the 'hint' function in Snowpark to provide a join hint that suggests a specific join strategy to the Snowflake query optimizer.
• D. Apply a more selective 'filter operation to the 'sales_df' DataFrame before the 'groupBy' operation, reducing the amount of data that needs to be shuffled.
• E. Increase the warehouse size to provide more resources for data processing, which will inherently reduce data shuffling.

You have a Snowpark DataFrame containing sensor data'. You need to write this data to a Snowflake stage 'sensor_stage' , creating a new set of files every hour based on the 'timestamp' column (data type: Timestamp). You also want to ensure that the file names include the hour of the timestamp and are written in Avro format with Zstandard compression. The directory structure on the stage should reflect the hourly partitioning. Which of the following approaches offers the most efficient and scalable way to achieve this, while minimizing the number of files written per hour?

• A. Create a new DataFrame by adding an 'hour' column extracted from the 'timestamp' column. Then use 'df.write.partitionBy('hour').format('avro').option('compression', 'zstd').mode('append').save('@sensor_stage/')'.
• B. Define a stored procedure that iterates through hourly intervals, filters the DataFrame based on the current hour, and writes the filtered DataFrame to the stage using 'df.write.format('avro').option('compression', 'zstd').mode('append').save(f'@sensor_stage/hour={current_hour}/')'
• C. Create a view on top of the data and schedule a task which create file in avro with zstd compression by running the select statment with group by hour.
• D. Write a Python script that connects to Snowflake, retrieves the entire DataFrame, iterates through each row, determines the hour from the 'timestamp', and writes each row to a separate Avro file named after the hour in the 'sensor_stage'
• E. Using scala user defined function (UDF) for write dataframe into stage in avro file format partitioned by Hour and calling it in snowpark dataframe.

Consider the following scenario: You need to implement a UDF in Snowpark Python to calculate the distance between two geographical coordinates (latitude and longitude). The UDF should handle potential null values gracefully and return null if either input coordinate is null. Which code snippet demonstrates the MOST efficient and correct implementation, leveraging Snowpark's capabilities?

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You are using Snowpark Python to process a DataFrame containing customer data,. One of the columns, 'phone_number' , contains phone numbers in various formats (e.g., '123-456-7890', '(123) 456-7890', '1234567890'). You need to standardize these phone numbers to the format 'XXX-XXX-XXXX' using a User-Defined Function (UDF). You want to create a UDF called 'standardize_phone_number' that takes a string as input and returns the standardized phone number. Which of the following code snippets correctly defines and registers this UDF in Snowpark, and applies it to the 'phone_number' column of the 'customer df DataFrame? Assume a Snowflake session object called 'session' is already available.

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